Computer Sciences Commons^™

Information Systems: No Boundaries! A Concise Approach To Understanding Information Systems For All Disciplines, Shane M. Schartz

All Open Educational Resources

This book was created to provide a different experience for students beginning their studies in information systems. Instead of being bombarded with information from a business systems perspective, the goal of this book is to provide a baseline of material regarding information systems in all disciplines, not just business systems - hence the name No Boundaries!

Lightweight Encryption Based Security Package For Wireless Body Area Network, Sangwon Shin

Electronic Theses and Dissertations

As the demand of individual health monitoring rose, Wireless Body Area Networks (WBAN) are becoming highly distinctive within health applications. Nowadays, WBAN is much easier to access then what it used to be. However, due to WBAN’s limitation, properly sophisticated security protocols do not exist. As WBAN devices deal with sensitive data and could be used as a threat to the owner of the data or their family, securing individual devices is highly important. Despite the importance in securing data, existing WBAN security methods are focused on providing light weight security methods. This led to most security methods for WBAN …

Plant Species Identification In The Wild Based On Images Of Organs, Meghana Kovur

Graduate Theses, Dissertations, and Problem Reports (ETD)

Image-based plant species identification in the wild is a difficult problem for several reasons. First, the input data is subject to a very high degree of variability because it is captured under fully unconstrained conditions. The same plant species may look very different in different images, while different species can often appear very similar, challenging even the recognition skills of human experts in the field. The large intra-class and small inter-class image variability makes this a fine-grained visual classification problem. One way to cope with this variability and to reduce image background noise is to predict species based on the …

Ensemble Encoder-Decoder Models For Predicting Land Transformation, Pariya Pourmohammadi

Graduate Theses, Dissertations, and Problem Reports (ETD)

In studying dynamic and complex processes which are influenced by a system of inter-connected driving variables, it is crucial to apply models that can learn the complexity of the interactions. Land transformation is one of such complex processes, prediction of which can help to mitigate severe climate situations and improve the resiliency of communities. In this study, a multi-spectral set of data cubes is used to capture various characteristics of a geographic region. Based on the data cube, a feature space is constructed using socio-economic attributes, terrain characteristics, and landscape traits of the study region. Two-dimensional and three-dimensional convolutional neural …

Deep Fingerprint Matching From Contactless To Contact Fingerprints For Increased Interoperability, Alexander James Wilson

Graduate Theses, Dissertations, and Problem Reports (ETD)

Contactless fingerprint matching is a common form of biometric security today. Most smartphones and associated apps now let users opt into using this form of biometric security. However, it’s difficult to match a finger-photo to a fingerprint because of perspective distortion occurring at the edges of the finger-photo, so direct matching using conventional methods will not be as accurate due to a lack of sufficient matching minutiae points. To address this issue, we propose a deep model, Perspective Distortion Rectification Model (PDRM), to estimate the fingerprint correspondence for finger-photo images in order to recover more minutiae points. Not only do …

Modified Firearm Discharge Residue Analysis Utilizing Advanced Analytical Techniques, Complexing Agents, And Quantum Chemical Calculations, William J. Feeney

Graduate Theses, Dissertations, and Problem Reports (ETD)

The use of gunshot residue (GSR) or firearm discharge residue (FDR) evidence faces some challenges because of instrumental and analytical limitations and the difficulties in evaluating and communicating evidentiary value. For instance, the categorization of GSR based only on elemental analysis of single, spherical particles is becoming insufficient because newer ammunition formulations produce residues with varying particle morphology and composition. Also, one common criticism about GSR practitioners is that their reports focus on the presence or absence of GSR in an item without providing an assessment of the weight of the evidence. Such reports leave the end-used with unanswered questions, …

Identification And Classification Of Radio Pulsar Signals Using Machine Learning, Di Pang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated single-pulse search approaches are necessary as ever-increasing amount of observed data makes the manual inspection impractical. Detecting radio pulsars using single-pulse searches, however, is a challenging problem for machine learning because pul- sar signals often vary significantly in brightness, width, and shape and are only detected in a small fraction of observed data.

The research work presented in this dissertation is focused on development of ma- chine learning algorithms and approaches for single-pulse searches in the time domain. Specifically, (1) We developed a two-stage single-pulse search approach, named Single- Pulse Event Group IDentification (SPEGID), which automatically identifies and clas- …

Maidrl: Semi-Centralized Multi-Agent Reinforcement Learning Using Agent Influence, Anthony Lee Harris

Graduate Theses/Dissertations

In recent years, reinforcement learning algorithms, a subset of machine learning that focuses on solving problems through trial-and-error learning, have been used in the field of multi-agent systems to help the agents with interactions and cooperation on a variety of tasks. Given the enormous success of reinforcement learning in single-agent systems like Chess, Shogi, and Go, it is natural for the next step to be the expansion into multi-agent systems. However, controlling multiple agents simultaneously is extremely challenging, as the complexity increases tremendously with the number of agents in the system. Existing approaches in this regard use a wide range …

A Serious Game For Social Engineering Awareness Creation, Fabian Muhly, Philipp Leo, Stefano Caneppele

Journal of Cybersecurity Education, Research and Practice

Social engineering is a method used by offenders to deceive their targets utilizing rationales of human psychology. Offenders aim to exploit information and use them for intelligence purposes or financial gains. Generating resilience against these malicious methods is still challenging. Literature shows that serious gaming learning approaches are used more frequently to instill lasting retention effects. Serious games are interactive, experiential learning approaches that impart knowledge about rationales and concepts in a way that fosters retention. In three samples and totally 97 participants the study at hand evaluated a social engineering serious game for participants’ involvement and instruction compliance during …

5g Security Challenges And Solutions: A Review By Osi Layers, S. Sullivan, Alessandro Brighente, Sathish Kumar

Electrical and Computer Engineering Faculty Publications

The Fifth Generation of Communication Networks (5G) envisions a broader range of servicescompared to previous generations, supporting an increased number of use cases and applications. Thebroader application domain leads to increase in consumer use and, in turn, increased hacker activity. Dueto this chain of events, strong and efficient security measures are required to create a secure and trustedenvironment for users. In this paper, we provide an objective overview of5G security issues and theexisting and newly proposed technologies designed to secure the5G environment. We categorize securitytechnologies usingOpen Systems Interconnection (OSI)layers and, for each layer, we discuss vulnerabilities,threats, security solutions, challenges, gaps …

Development Of A Real Time Human Face Recognition Software System, Askar Boranbayev, Seilkhan Boranbayev, Mukhamedzhan Amirtaev, Malik Baimukhamedov, Askar Nurbekov

Physics & Astronomy Faculty Publications

In this study, a system for real-time face recognition was built using the Open Face tools of the Open CV library. The article describes the methodology for creating the system and the results of its testing. The Open CV library has various modules that perform many tasks. In this paper, Open CV modules were used for face recognition in images and face identification in real time. In addition, the HOG method was used to detect a person by the front of his face. After performing the HOG method, 128 face measurements were obtained using the image encoding method. A convolutional …

Statistical Modeling Of Hpc Performance Variability And Communication, Jered B. Dominguez-Trujillo

Computer Science ETDs

Understanding the performance of parallel and distributed programs remains a focal point in determining how compute systems can be optimized to achieve exascale performance. Lightweight, statistical models allow developers to both characterize and predict performance trade-offs, especially as HPC systems become more heterogeneous with many-core CPUs and GPUs. This thesis presents a lightweight, statistical modeling approach of performance variation which leverages extreme value theory by focusing on the maximum length of distributed workload intervals. This approach was implemented in MPI and evaluated on several HPC systems and workloads. I then present a performance model of partitioned communication which also uses …

What Makes A Popular Academic Ai Repository?, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan, Shanping Li

Research Collection School Of Computing and Information Systems

Many AI researchers are publishing code, data and other resources that accompany their papers in GitHub repositories. In this paper, we refer to these repositories as academic AI repositories. Our preliminary study shows that highly cited papers are more likely to have popular academic AI repositories (and vice versa). Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers. We collect 1,149 academic AI repositories, in which we label the top 20% repositories that have the most number of stars as popular, and …

Creators And Backers In Rewards-Based Crowdfunding: Will Incentive Misalignment Affect Kickstarter's Sustainability?, Michael Wessel, Rob Gleasure, Robert John Kauffman

Research Collection School Of Computing and Information Systems

Incentive misalignment in rewards-based crowd-funding occurs because creators may benefit disproportionately from fundraising, while backers may benefit disproportionately from the quality of project deliverables. The resulting principal-agent relationship means backers rely on campaign information to identify signs of moral hazard, adverse selection, and risk attitude asymmetry. We analyze campaign information related to fundraising, and compare how different information affects eventual backer satisfaction, based on an extensive dataset from Kickstarter. The data analysis uses a multi-model comparison to reveal similarities and contrasts in the estimated drivers of dependent variables that capture different outcomes in Kickstarter’s funding campaigns, using a linear probability …

Fakespotter: A Simple Yet Robust Baseline For Spotting Ai-Synthesized Fake Faces, Run Wang, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yihao Huang, Jian Wang, Yang Liu

Research Collection School Of Computing and Information Systems

In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial images which brings potential security concerns to humans as the fakes spread and fuel the misinformation. However, robust detectors of these AI-synthesized fake faces are still in their infancy and are not ready to fully tackle this emerging challenge. In this work, we propose a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AIsynthesized fake faces. The studies on neuron coverage and interactions have successfully shown that they can be served as testing …

Integration And Development Of Learning Management Features Into The Colums Platform, Samer Qahtan Hameed, Tam Sakirin, Yahya Hakami

Mesopotamian Journal of Computer Science

Due to the increasing use of computerized information systems in higher and further education for both administrative (Human Resources, Finance, Student Records) and instructional (Teaching, Learning, and Research) purposes, the challenge of systems integration has emerged like Virtual Learning Environments, electronic resource discovery tools, etc. COLUMS is a comprehensive and deeply integrated software product for large, medium, and small educational institutions that automates data stream and can be thought of as an Organization Wide Computing Package.  The concept of COLUMS is to interconnect Students, Teachers, Parents and Management in effective manner. To meet all the requirements of the customer, the …

Character Recognition By Implementing Fpga-Based Artificial Neural Network, Ahmed Hussein Ali, Mostafa Abdulghfoor Mohammed, Munef Abdullah Ahmed

Mesopotamian Journal of Computer Science

A non-linear applied math knowledge modelling tool, Artificial Neural Networks (ANN) are predominantly used to model complicated interactions between inputs and outputs or to look for patterns within the data. Using VHDL coding, we developed a generic hardware-based ANN. This classifier has been trained to recognize letters on a 4x4 binary grid that a user fills out using 16 toggle switches. An LCD shows the most likely classification that the ANN proposed. The ANN was taught to recognize 20 English character patterns and 9 Arabic character patterns on a 4x4 grid to showcase the viability of the FPGA execution of …

Character Recognition Techniques And Approaches: A Literature Review, Mostafa Abdulghfoor Mohammed, Shuyuan Yang

Mesopotamian Journal of Computer Science

Researchers have carried out many approaches to recognizing the OCR through software-based and FPGA-based and its relationship with the ANNs and training these NNs by using the BP algorithm. The FPGA supports high speed to recognize the character because it works in parallel. It involves many logic circuits and can process at the same time (clock cycle). There have been problems with the established system because the size, shape, and style of supposedly identical characters might differ from person to person and even within the same person on rare occasions. The photograph is vulnerable to noise and can lose some …

Learning Management System Developments And Challenges: A Literature Review, Samer Qahtan Hameed, Hind Salman Hasan

Mesopotamian Journal of Computer Science

Learning Management Systems (LMS) are web-based software that is used to create and deliver educational content in a controlled and measurable way. They are designed for an LMS-specific teaching process. These systems are available in different forms and can be purchased as open source or closed source solutions. This paper provides an explanation for the LMS and the approach used to implement it. We introduce and describe background study of LMS and discusses the advantages and disadvantages of LMS. Then the study on other LMS existing systems that have the same purpose of this project and explains the advantages and …

An Empirical Examination Of The Computer Security Behaviors Of Telecommuters Working With Confidential Data Through Leveraging The Factors From Fear Appeals Model (Fam), Titus Dohnfon Fofung

CCAC Theses and Dissertations

Computer users’ security compliance behaviors can be better understood by devising an experimental study to examine how fear appeals might impact users’ security behavior. Telecommuter security behavior has become very relevant in information systems (IS) research with the growing number of individuals working from home. The increasing dependence on telecommuting to enhance the viability and convenience has created an urgency with the advent of the COVID-19 pandemic to examine the behavior of users working at home across a corporate network. The home networks are usually not as secure as those in corporate settings. There is seldom a firewall setting and …

Risk-Averse Optimal Bidding Strategy For A Wind Energy Portfolio Managerincluding Ev Parking Lots For Imbalance Mitigation, Alper Çi̇çek, Ozan Erdi̇nç

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, an optimal bidding strategy for a wind energy portfolio manager (WEPM) including electricvehicle parking lots (EVPLs) that aims to maximize profits by trading in the day-ahead (DA) market and balancingmarket (BM) and through bilateral contracts, taking into account line capacities and risk management is proposed. Thementioned structure is modeled in mixed integer linear programming (MILP) framework, and the uncertainties regardingelectric vehicle (EV) behavior, electricity market data, wind power generation are captured via a stochastic approach. Todemonstrate the effectiveness of the model, several case studies are carried out considering Sweden and Turkey electricitymarket prices with different risk aversion …

A New Hybrid Genetic Algorithm For Protein Structure Prediction On The 2dtriangular Lattice, Bouroubi Sadek, Nabil Boumedine

Turkish Journal of Electrical Engineering and Computer Sciences

The flawless functioning of the protein is essentially related to its three-dimensional structure. Therefore,predicting protein structure from its amino acid sequence is a fundamental problem that draws researchers' attentionin many areas. The protein structure prediction problem (PSP) can be formulated as a combinatorial optimization problem based on simplified lattice models such as the hydrophobic-polar model. In this paper, we propose a new hybridalgorithm that combines three different known heuristic algorithms: the genetic algorithm, the tabu search strategy,and the local search algorithm to solve the PSP problem. Regarding the evaluation of the proposed approach, wepresent an experimental study, where we consider …

Development Of Majority Vote Ensemble Feature Selection Algorithm Augmentedwith Rank Allocation To Enhance Turkish Text Categorization, Emi̇n Borandağ, Akin Özçi̇ft, Yeşi̇m Kaygusuz

Turkish Journal of Electrical Engineering and Computer Sciences

The increase in the number of texts as digital documents from numerous sources such as customer reviews,news, and social media has made text categorization crucial in order to be able to manage the enormous amount ofdata. The high dimensional nature of these texts requires a preliminary feature selection task to reduce the featurespace with a potential increase in the prediction accuracy. In this study, we developed an ensemble feature selectionmethod, namely majority vote rank allocation, was developed for Turkish text categorization purposes. The methoduses a majority voting ensemble strategy in combination with a rank allocation approach to combine weak filters …

Efficient Hybrid Passive Method For The Detection And Localization Of Copy-Moveand Spliced Images, Navneet Kaur, Neeru Jindal, Kulbir Singh

Turkish Journal of Electrical Engineering and Computer Sciences

Digital passive image forgery methods are extensively used to verify the authenticity and integrity of images.Splicing and copy-move are the most common types of passive digital image forgeries. Several approaches have beenproposed to detect these forgeries separately, but very few approaches are available that can detect them simultaneously.However, a more e?icient method is still in demand to meet the day-to-day challenges to detect these forgeries at thesame time. So, a passive hybrid approach based on discrete fractional cosine transform (DFrCT) and local binarypattern (LBP) is proposed to detect copy-move and splicing forgeries simultaneously. The extra parameter i.e. fractionalparameter of DFrCT …

Neuro-Adaptive Backstepping Integral Sliding Mode Control Design For Nonlinearwind Energy Conversion System, Imran Ullah Khan, Laiq Khan, Qudrat Khan, Shafaat Ullah, Uzair Khan, Saghir Ahmad

Turkish Journal of Electrical Engineering and Computer Sciences

The electrical power extracted from a wind energy conversion system (WECS) tends to be inconsistentdue to the intermittent nature of the wind. This issue is addressed by formulating a maximum power point tracking(MPPT) control strategy that optimizes the power extraction from the WECS under a wide range of wind speed profiles.This research article focuses on the formulation of a nonlinear neuro-adaptive backstepping integral sliding mode control(NABISMC) based MPPT strategy for a standalone, variable speed, fixed-pitch WECS equipped with a permanentmagnet synchronous generator (PMSG). The proposed paradigm is a hybrid of the conventional backstepping andthe integral sliding mode control (ISMC) based …

Impact Of Image Segmentation Techniques On Celiac Disease Classification Usingscale Invariant Texture Descriptors For Standard Flexible Endoscopic Systems, Manarbek Saken, Munkhtsetseg Banzragch Yağci, Nejat Yumuşak

Turkish Journal of Electrical Engineering and Computer Sciences

Celiac disease (CD) is quite common and is a proximal small bowel disease that develops as a permanentintolerance to gluten and other cereal proteins in cereals. It is considered as one of the most di?icult diseases to diagnose.Histopathological evidence of small bowel biopsies taken during endoscopy remains the gold standard for diagnosis.Therefore, computer-aided detection (CAD) systems in endoscopy are a newly emerging technology to enhance thediagnostic accuracy of the disease and to save time and manpower. For this reason, a hybrid machine learning methodshave been applied for the CAD of celiac disease. Firstly, a context-based optimal multilevel thresholding technique wasemployed …

Ensemble Learning Of Multiview Cnn Models For Survival Time Prediction Of Braintumor Patients Using Multimodal Mri Scans, Abdela Ahmed Mossa, Ulus Çevi̇k

Turkish Journal of Electrical Engineering and Computer Sciences

Brain tumors have been one of the most common life-threatening diseases for all mankind. There have beenhuge efforts dedicated to the development of medical imaging techniques and radiomics to diagnose tumor patients quicklyand e?iciently. One of the main aims is to ensure that preoperative overall survival time (OS) prediction is accurate.Recently, deep learning (DL) algorithms, and particularly convolutional neural networks (CNNs) achieved promisingperformances in almost all computer vision fields. CNNs demand large training datasets and high computational costs.However, curating large annotated medical datasets are difficult and resource-intensive. The performances of singlelearners are also unsatisfactory for small datasets. Thus, this study …

Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia

Turkish Journal of Electrical Engineering and Computer Sciences

The main purpose of this paper is to process key information in medical text records and also classifypatients, per different levels of breast imaging-reporting and data system (BI-RADS). The BI-RADS is a scheme for thestandardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reportssupported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction fromtextual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosingkeywords from medical text reports. Word2vec and term frequency inverse document frequency (TFIDF) techniques areused for extracting features, finally, they are combined …

Robust Image Hashing Based On Structural And Perceptual Features Forauthentication Of Color Images, Muhammad Farhan Khan, Syed Muhammad Monir, Imran Naseem

Turkish Journal of Electrical Engineering and Computer Sciences

Image hashing is one of the most celebrated techniques regarding the discipline of image forensics, imageretrieval, image indexing, content verification, and zero watermarking. For such sensitive and complex problems,generation of a unique and robust image hash is an utmost prerequisite for an image identifier driven from the perceptualcontents of an image. As a design perspective, it is essential for an image hash to have robustness and optimizeddiscriminative capability. We propose a robust image hashing technique by acquiring perceptual features based on anovel distance magnitude profile utilizing color pixel incongruity among the contiguous pixels, as well as producing astructural image for …

Low Communication Parallel Distributed Adaptive Signal Processing (Lc-Pdasp)Architecture For Processing-Inefficient Platforms, Hasan Raza, Ghalib Hussain, Noor Khan

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a low communication parallel distributed adaptive signal processing (LC-PDASP) architecturefor a group of computationally incapable and inexpensive small platforms is introduced. The proposed architectureis capable of running computationally high adaptive filtering algorithms parallely with minimally low communicationoverhead. A recursive least square (RLS) adaptive algorithm based on the application of multiple-input multiple-output(MIMO) channel estimation is implemented on the proposed LC-PDASP architecture. Complexity and Communicationburden of proposed LC-PDASP architecture are compared with that of conventional PDASP architecture. The compar-ative analysis shows that the proposed LC-PDASP architecture exhibits low computational complexity and provides animprovement more than of85%reduced communication burden than …